Disparate data sources and data types can introduce significant problems to corporate employees tasked with consolidating information into cognizable reports, when attempting to ascertain the health of the corporation, for example. Moreover, the costs and resources required for determining and monitoring the “wellness” (or lack thereof) of the company can be significant. For example, metrics involved with the generation of key performance indicators (KPIs) provide a means for assembling a scorecard to assist a company in defining and measuring corporate wellness by assessing progress toward organizational as well as corporate goals.
In one complex business analysis example, the business user needs to report on the health of a company by compiling and analyzing information over four different corporate business groups (e.g., finances, customers, staffing, and employee satisfaction). This information can be stored in different types of data sources such as multidimensional and/or tabular lists. Scorecards and dashboards provide a mechanism for tapping into this data in order to provide a high-level view of various interesting aspects of the corporate health. Dashboards may contain several reports or scorecards. The data sources on which each report is based may be different for each report or scorecard. Consumers of these dashboards want to view current data, regardless of the origin of the report's underlying data. Consumers also want the ability to navigate time, for example, in reports using simple filter controls such as a calendar or select list. Designers of the dashboard want the ability to align time dimensions between multiple cubes and perspectives and to define time dimensionality for tabular (table or view) data. Accordingly, as a means to reduce costs and resources for accessing and analyzing corporate well-being, businesses desire ways in which to provide business users a quick and easy mechanism for working with complex business processes.